Combinatorial Structures in Nonlinear Programming

Combinatorial Structures in Nonlinear Programming Stefan Scholtes¤ April 2002 Abstract Non-smoothness and non-convexity in optimization problems often arise because a combinatorial structure is imposed on smooth or convex data. The combinatorial aspect can be explicit, e.g. through the use of ”max”, ”min”, or ”if” statements in a model, or implicit as in the case of bilevel optimization where the combinatorial structure arises from the possible choices of active constraints in the lower level problem. In analyzing such problems, it is desirable to decouple the combinatorial from the nonlinear aspect and deal with them separately. This paper suggests a problem formulation which explicitly decouples the two aspects. We show that such combinatorial nonlinear programs, despite their inherent non-convexity, allow for a convex first order local optimality condition which is generic and tight. The stationarity condition can be phrased in terms of Lagrange multipliers which allows an extension of the popular sequential quadratic programming (SQP) approach to solve these problems. We show that the favorable local convergence properties of SQP are retained in this setting. The computational effectiveness of the method depends on our ability to solve the subproblems efficiently which, in turn, depends on the representation of the governing combinatorial structure. We illustrate the potential of the approach by applying it to optimization problems with max-min constraints which arise, for example, in robust optimization. 1 Introduction Nonlinear programming is nowadays regarded as a mature field. A combination of important algorithmic developments and increased computing power over the past decades have advanced the field to a stage where the majority of practical problems can be solved efficiently by commercial software. However, this is not to say that there are no research challenges left. One considerable challenge is posed by global, i.e. non-convex, optimization problems. There are two main sources of non- convexity: An analytic and a combinatorial source. Analytic non-convexity is due to a combination of negative and positive curvature of underlying model functions, ¤Judge Institute of Management & Department of Engineering, University of Cambridge, Cambridge CB2 1AG, [email protected] 1 such as polynomials or trigonometric functions. In contrast, combinatorial non- convexity arises when a combinatorial selection rule is imposed on possibly quite simple, even linear, functions. Typical examples are function evaluations that make use of ”max”, ”min”, ”abs”, or ”if” statements. Business spreadsheets are a ver- itable source of such combinatorial nonlinear functions. Consider for example the calculation of revenue as a function of price and production capacity. Revenue is the product of price and sales, where sales is the minimum of demand and production capacity. If capacity · and price ¼ are decision variables and demand d is a known function of price, then the revenue formula becomes r(¼; ·) = ¼ minfd(¼);·g: Such formulas are commonplace in spreadsheet models. A more complicated example where nested max- and min-terms occur is the resource allocation problem that arises when several projects are evaluated by decision trees and compete for limited resources. This is a timely problem area as decision tree analysis is experiencing a renaissance in the wake of the popularization of real options valuations of projects. Decision tree evaluations consist essentially of a finite sequence of nested linear combinations of max-terms, where the linear combinations correspond to taking expectations at event nodes and the max-terms correspond to choosing the best of a finite number of possible actions at decision nodes. The value of a project is likely to depend on design parameters such as marketing budgets or other limited resources and a company is faced with the problem of allocating such limited resources to a variety of projects. This resource allocation problem will involve parametric values of decision trees, i.e., sequences of max-terms. The problem becomes even more complex if, as is often the case in practice, probability estimates are subjective and several experts assign differing probability estimates to the event branches in the trees. Backward induction through the decision trees will lead to value estimates for each of these probability estimates and a conservative decision maker may well consider the minimum of the expected values as a proxy for the unknown value of the project at event nodes. With this addition, the constraints of the resource allocation problem involve sequences of max- as well as min-terms. These max- and min-terms define the combinatorial structure of the problem. Many practical applications are a combination of both, the analytical as well as the combinatorial sources of non-convexity. A non-convex combinatorial structure may well be imposed onto smooth but already non-convex functions. Never- theless, it is helpful to think of the two sources separately, not least because the combinatorial source introduces the additional complication of non-smoothness. In fact, for optimization problems which involve only the first, the analytic source of non-convexity, nonlinear programming provides us with a host of methods, such as sequential quadratic programming (SQP) or interior point methods. Engineers and operations researchers regularly apply these methods, albeit they cannot guarantee a global optimum, because the methods provide often substantial improvement and, at the same time, are applicable to model sizes that are orders of magnitude larger than those that can be handled by global optimization routines. It is indeed unlikely that the dominance of local optimization procedures will change in the foreseeable future. In this light, it is rather surprising that relatively little attention has been 2 given to the local optimization of non-convex problems arising from the second, the combinatorial source of non-convexity. This is the more surprising as some fairly general classes of such problems, such as disjunctive programs, have been studied extensively from a global optimization viewpoint. The present study attempts to fill this gap by extending the tools available for the local optimization of smooth non-convex problems to problems involving non-convex combinatorial structures. To date, problems of the type considered here are often tackled by variants of Lemaréchal’s bundle method [17], such as the bundle trust region code of Schramm and Zowe [26]. These methods were originally designed to solve non-smooth convex problems but they are well-defined for more general non-smooth problems if the sub- gradient concept is suitably broadened, see e.g. [5]. However, bundle methods have rather poor convergence properties for non-convex problems. This is not surprising since they are based on convex models, such as maxima of finitely many affine functions, of originally non-convex functions and can therefore only be guaranteed to converge to points where these convex models provide no descent directions. These methods do not exploit the specific combinatorial structure of the problem at hand, e.g. a particular combination of ”max” and ”min” terms, but rather replace this structure by a simpler and essentially convex combinatorial structure. An alter- native approach, more akin to our own and more suitable for the exploitation of non-convex structures, has been developed for piecewise linear problems under the label of simplex-type or active-set type methods by Fourer [10, 11, 12] and others. For recent further developments along these lines we refer the reader to the compre- hensive paper of Conn and Mongeau [6] which also surveys some of these methods and provides a fairly extensive bibliography. The existing combinatorial methods, however, are either restricted to piecewise affine models or to models where the combinatorial structure is inherently convex, such as the minimization of point-wise maxima of nonlinear functions. We attempt to add to this literature by presenting and analyzing a modelling framework which explicitly allows for non-convex structures as well as smooth nonlinear data. The focus of this paper is the extension of the traditional active set and SQP approaches to combinatorial nonlinear programs. We will provide a theoretical framework for the local optimization of such problems, including appropriate stationarity conditions that are tighter than the stationarity conditions of [5] which bundle methods rely on. We then show how the SQP-idea can be extended to combinatorial nonlinear programs and that these methods have better convergence properties for these problems than bundle-type methods, both in terms of speed and in terms of identifying more appropriate stationary points. We have already mentioned that one motivation for this study is to allow model- ers to use simple nonsmooth functions such as ”max”, ”min”, ”k:k”, etc. as building blocks in their optimization models. There are, however, also interesting classes of optimization problems to which our approach applies directly and we will mention some of them in the next section before we proceed to the technical developments. One of these classes are optimization problems with max-min constraints which 3 arise in robust optimization. We will use this special case frequently to illustrate our general theoretical developments. Section 3 introduces necessary extensions of standard NLP terminology, including notions of active constraint

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